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L4Q: Parameter Efficient Quantization-Aware Training on Large Language Models via LoRA-wise LSQ
In resource-constrained scenarios, PTQ, with its reduced training overhead, is often preferred over QAT, despite the latter's potential for higher accuracy.
Squeezing Large-Scale Diffusion Models for Mobile
The emergence of diffusion models has greatly broadened the scope of high-fidelity image synthesis, resulting in notable advancements in both practical implementation and academic research.
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